Cameras as Rays: Pose Estimation via Ray Diffusion (2402.14817v3)

Abstract: Estimating camera poses is a fundamental task for 3D reconstruction and remains challenging given sparsely sampled views (<10). In contrast to existing approaches that pursue top-down prediction of global parametrizations of camera extrinsics, we propose a distributed representation of camera pose that treats a camera as a bundle of rays. This representation allows for a tight coupling with spatial image features improving pose precision. We observe that this representation is naturally suited for set-level transformers and develop a regression-based approach that maps image patches to corresponding rays. To capture the inherent uncertainties in sparse-view pose inference, we adapt this approach to learn a denoising diffusion model which allows us to sample plausible modes while improving performance. Our proposed methods, both regression- and diffusion-based, demonstrate state-of-the-art performance on camera pose estimation on CO3D while generalizing to unseen object categories and in-the-wild captures.

• The paper introduces a ray-based camera parametrization that enables per-patch pose estimation from sparse views.
• It employs a regression-based approach enhanced with a denoising diffusion model to outperform traditional methods.
• Empirical results on the CO3D dataset validate its superior performance and potential for robust 3D reconstruction.

Advancements in Camera Pose Estimation: A Ray Diffusion Approach

Introduction to Camera Pose Estimation with Ray-Based Representation

The task of accurately estimating camera poses using sparsely sampled views has been a longstanding challenge in the field of 3D reconstruction. Classic methods and even recent learning-based approaches have primarily focused on inferring global camera parametrizations directly from image inputs. However, this conventional technique has shown limitations, particularly in scenarios where the views are sparse. In addressing this, the paper presents an innovative approach by proposing a distributed representation of camera pose that conceptualizes a camera as a bundle of rays. This ray-based representation aligns with the notion that leveraging distributed representations may be more conducive for neural learning, a paradigm that benefits from associations across image patches or pixels.

Novel Contributions

The core contributions laid out in the paper can be summarized as follows:

• Ray-Based Camera Parametrization: The introduction of an alternative approach for pose prediction facilitates per-patch ray equations, shifting away from the traditional method of inferring global camera parametrizations.
• Regression-Based Approach: The paper delineates a simple yet effective regression-based method for deducing this representation from sparsely sampled views, showcasing superior performance over existing state-of-the-art pose prediction methodologies.
• Denoising Diffusion Model: The regression-based method was further extended to include a denoising diffusion model. This adaptation not only bolstered performance but also showed capability in capturing the distribution over cameras, thereby addressing inherent ambiguities arising from sparse-view pose estimation.

Methodology Insight

The superiority of a ray-based representation over traditional global parametrizations is thoroughly explored through a systematic approach involving the conversion between camera-to-ray and ray-to-camera representations. This method elegantly addresses the challenges posed by sparse-view scenarios by optimizing a least-square objective given a predicted bundle of rays. The detailed methodology encompasses two key aspects: pose estimation via ray regression and enhancement through denoising ray diffusion. These steps are pivotal in addressing uncertainties inherent in sparsely sampled views, thereby enabling the model to sample plausible modes efficiently.

Empirical Evaluation and Results

Empirical evaluations conducted on the CO3D dataset highlighted the proposed methods' effectiveness, with both regression- and diffusion-based approaches demonstrating state-of-the-art performance in camera pose estimation tasks. Notably, the ray diffusion model emerged as particularly powerful, outperforming all baseline methods and even the ray regression approach under certain metrics. These results underscore the potential of ray-based camera representations and denoising diffusion models in refining camera pose estimation, especially in challenging sparse-view settings.

Future Directions and Implications

This work opens several avenues for future exploration in 3D reconstruction and camera pose estimation. The efficacy of distributed ray representations, coupled with the transformative potential of denoising diffusion models, could redefine approaches to camera pose estimation, particularly in complex real-world scenarios with limited views. Incorporating geometric consistency constraints within the distributed ray representation framework could further harness the synergy between classical pose estimation pipelines and learning-based methods, potentially leading to more accurate and robust systems.

Additionally, the presented methodologies' successful application beyond CO3D to in-the-wild captures indicates a promising direction for deploying these advancements in various practical and research-focused applications.

Acknowledgements and Support

The research acknowledges contributions and feedback from several collaborators and is supported by grants and awards from significant institutions, highlighting the collaborative and interdisciplinary nature of advancements in generative AI and LLMs.

In conclusion, the paper sets a new benchmark in the field of camera pose estimation by proposing and validating the efficacy of a ray-based distributed representation coupled with denoising diffusion models. This work not only addresses immediate challenges in sparse-view pose inference but also paves the way for broader applications and future innovations in 3D reconstruction technologies.